Quantitative Quality Score for Software

Nandakumar, R.

doi:10.1145/3511430.3511457

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…In a different area of software quality, one research [48] suggests using crowdsourced workers, who meet minimum quality requirements, as a third party to evaluate and certify software meant for public users based on the available software documents and templates. Then, a quantitative quality score is assigned to the software-specific version, and the evaluation process is repeated after each considerable change in the current software version.…”

Section: Liu Et Al Inmentioning

confidence: 99%

A Review of Trending Crowdsourcing Topics in Software Engineering Highlighting Mobile Crowdsourcing and AI Utilization

Alghasham¹,

Alzakan²,

Al-Hagery³

2023

IJACSA

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Today's modern technologies and requirements make the utilization of crowdsourcing more viable and applicable. It is one of the problem-solving models that can be used in various domains to reduce costs and time. It is also an excellent way to find new and different ideas and solutions. This paper studies the use of crowdsourcing in software engineering and reveals adequate details to highlight its significance. A few recent literature reviews have been published to address specific topics or study general attributes of papers in crowdsourced software engineering. This paper, however, explores all recent publications related to software and crowdsourcing to find the trends and highlight mobile and AI usage in software crowdsourcing. The findings of this paper show that most research papers are in the areas of software management and software verification and validation. The results also reveal that machine learning and data mining techniques are predominant in software management crowdsourcing and software verification and validation. Furthermore, this study shows that the methods and techniques used in general crowdsourcing apply to mobile crowdsourcing except in mobile testing, where there is a need for clustering and prioritization of test reports.

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Section: Liu Et Al Inmentioning

confidence: 99%

A Review of Trending Crowdsourcing Topics in Software Engineering Highlighting Mobile Crowdsourcing and AI Utilization

Alghasham¹,

Alzakan²,

Al-Hagery³

2023

IJACSA

View full text Add to dashboard Cite

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Requirement Change Prediction Model for Small Software Systems

et al. 2023

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The software industry plays a vital role in driving technological advancements. Software projects are complex and consist of many components, so change is unavoidable in these projects. The change in software requirements must be predicted early to preserve resources, since it can lead to project failures. This work focuses on small-scale software systems in which requirements are changed gradually. The work provides a probabilistic prediction model, which predicts the probability of changes in software requirement specifications. The first part of the work considers analyzing the changes in software requirements due to certain variables with the help of stakeholders, developers, and experts by the questionnaire method. Then, the proposed model incorporates their knowledge in the Bayesian network as conditional probabilities of independent and dependent variables. The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. The model was evaluated by sensitivity analysis and comparison methods. For a given dataset, the proposed model computed the low state revisions probability to 0.42, and the high state revisions probability to 0.45. Thus, the results proved that the proposed approach can predict the change in the requirements document accurately by outperforming existing models.

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Quantitative Quality Score for Software

Cited by 2 publications

References 3 publications

A Review of Trending Crowdsourcing Topics in Software Engineering Highlighting Mobile Crowdsourcing and AI Utilization

A Review of Trending Crowdsourcing Topics in Software Engineering Highlighting Mobile Crowdsourcing and AI Utilization

Requirement Change Prediction Model for Small Software Systems

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